characteristics of the main molecular · pdf filech chorionamnionitis horionamnionīts cpap...

Ilze Kreicberga

CHARACTERISTICS OF THE MAIN MOLECULAR PROCESSES IN THE TISSUES OF PLACENTAS OF VARIOUS

GESTATIONAL AGES

Speciality – Neonatology

Summary of the Doctoral Thesis for obtaining the degree of a Doctor of Medicine

Riga, 2014

Doctoral Thesis worked out in: the Institute of Anatomy and Anthropology (IAA) of the Rīga Stradiņš University (RSU), Department of Obstetrics and Gynaecology (DOG) of RSU and Riga Maternity hospital.

Scientific supervisors of Thesis: Dr. habil. med. Professor Māra Pilmane, IAA, RSUDr. med. Professor Dace Rezeberga, DOG, RSU

Preferred Reviewers: Dr. med. Associate Professor Ilze Štrumfa, RSU, LatviaDr. med. Professor Daiva Vaitkiene, Lithuanian University of Health Sciences, Kaunas, LithuaniaDr. med. Professor Andres Arend, University of Tartu, Estonia

Dissertation will be defended on the 19th of May, 2014 at 15.30 in an open meeting of the Doctoral Council of Medicine of Rīga Stradiņš University, Riga, 16 Dzirciema Street, in the Lecture theatre Hippocrates.

Dissertation is available in the library of RSU and RSU homepage: www.rsu.lv

The Doctoral Thesis are developed with the support of the ESF project “Support to implementation of doctoral study programmes and obtaining the scientific degree in RSU”

Secretary of the Promotion Council: Dr. med. Professor Valda Staņēviča

3

TABLE OF CONTENTS

Abbreviations ................................................................................................ 4

1. Relevance of the study ................................................................................ 6

2. Aim of the study .......................................................................................... ..10

3. Study tasks ................................................................................................. 11

4. Novelty of the study .................................................................................... 12

5. Materials and methods ............................................................................... 13

6. Results ........................................................................................................ 16

6.1 Maternal parameters ......................................................................... 16

6.2 Neonatal parameters .......................................................................... 17

6.3 Placental parameters ......................................................................... 21

6.4 Routine microscopy of placental preparations .................................. 23

6.5 IHC research of placental samples ..................................................... 25

7. Discussion .................................................................................................. 42

7.1 Anthropology ..................................................................................... 42

7.2 Appropriateness for the gestation ...................................................... 45

7.3 Morphology ....................................................................................... 46

7.4 Clinical data ....................................................................................... 49

8.Conclusions ................................................................................................. 51

9.References ................................................................................................... 54

10.Publications and presentations .................................................................. 72

11.Acknowledgments ..................................................................................... 78

4

ABBREVIATIONS English Latvian

AGA Appropriate for the gestational age Atbilstošs grūtniecības laikam

BMI Body mass index Ķermeņa masas indekss

bFGF Basic Fibroblast growth factor

Bāziskais Fibroblastu augšanas faktors

CH Chorionamnionitis Horionamnionīts

CPAP Continuous positive airway pressure

Pastāvīgi pozitīvs spiediens elpceļos

CS Cesarean section Ķeizargrieziens

DAB Diaminobenzidine Diaminobenzidīns

ECM Extracellular matrix Ekstracellulārā matrica

FGFR Fibroblast growth factor receptor

Fibroblastu augšanas faktora receptors

GBS Group B Streptococcus B grupas streptokoks

HE Hematoxylin and eosin Hematoksilīns un eozīns

HGF Hepatocyte growth factor Hepatocītu augšanas faktors

HIER Heat induced epitope retrieval Karstuma izraisīta epitopu izdalīšana

HIV Human immunodeficiency virus Cilvēka imūndeficīta vīruss

IGF1 Insulin-like growth factor Insulīnam līdzīgais augšanas factors

IGF1R Insulin-like growth factor receptor

Insulīnam līdzīgā augšanas faktora receptors

IHC Immunohistochemistry Imunohistoķīmija

IL Interleukin Interleikīns

IUGR Intrauterine growth restriction Intrauterīnās augšanas aizture

5

English Latvian

LGA Large for the gestational age Liels grūtniecības laiks

LPS Lipopolysaccharide Lipopolisaharīds

MMP Matrix metaloproteinase Matricas metālproteināze

NICU Neonatal intensive care unit Jaundzimušo intensīvās terapijas nodaļa

NS Non-significant Nenozīmīgs

pH Power of Hydrogen ion H+ Ūdeņražu jonu H+ spēks

PBS Phosphate buffered saline Fosfātu bufera sāls šķīdums

pO2 Partial pressure of oxygen Skābekļa parciālais spiediens

PI Ponderal index Ponderela indekss

PPROM Pre-term premature rupture of membranes

Pirms-termiņa priekšlaicīgs augļa apvalku plīsums

PROM Premature rupture of membranes

Priekšlaicīgs augļa apvalku plīsums

RNA Ribonucleic acid Ribonukleīnskābe

ROM Rupture of membranes Augļa apvalku plīsums

RSU Riga Stradins university Rīgas Stradiņa universitāte

SGA Small for the gestational age Mazs grūtniecības laiks

TUNEL Terminal deoxynucleotidyl transferase dUTP nick end labeling

Terminālās deoksinukleotidiltransferāzes dUTP N gala marķēšana

TGF Transforming growth factor Transformējošais augšanas faktors

TNFα Tumor necrosis factor Tumora nekrozes faktors

VEGF Vascular endothelial growth factor

Asinsvadu endotēlija augšanas faktors

6

1. RELEVANCE OF THE STUDY

Perinatal and neonatal mortality are important indicators of the

development of a country and highly developed ones tend to maintain those

figures low, permanently auditing childbirth management related issues

(Mancey-Jones et al., 1997; Pattinson et al., 2009) and implementing suggested

improvements. In the developing countries causes of mortality quite often

appear to be evident and are highly associated with availability of qualified

medical care while in the developed ones problems quite often are hidden in

the maternal-fetal unit; investigations of this environment and identification of

potentially disadvantageous processes can give clues for the achievement of

better outcomes. The most commonly seen problems, possibly leading to

adverse pregnancy outcomes, are pre-term premature rupture of membranes,

leading to pre-term labor (Al-Riyami et al., 2013), fetal growth failure due to

various causes (Longo et al., 2013) and events, leading to significant fetal

distress and threat to his life (Ribak et al., 2011).

Fetal growth and development is determined by the interaction of

mother and fetus by the means of interface placenta throughout the pregnancy;

disturbances in regulation of fetal growth and development can result in

adverse outcomes for the neonate, and these adverse outcomes may persist into

adult life. Placenta ensures circumstances for fetal growth and development

(Bauer et al., 1998; Murphy et al., 2006, Jansson et al., 2007) and findings in its

tissues reveal processes, having determined the outcome of pregnancy; they

have special clinical significance in the cases of high risk pregnancies or

unexpected complications. Understanding the physiological and pathological

processes in placenta disclose the roads of problem solving with a possibly

better perinatal outcome.

7

Antenatal care as well as post-delivery examination of placenta has

become a routine part of the perinatal (obstetrical and neonatal) care for the

assessment of pathways, possibly leading or having led to an unwanted

outcome. Even routine examination of a post–delivery placenta provides

significant information on the fetal environment (Fox and Sebire, 2007; Tomas

et al., 2010; Roescher et al., 2011; Roje et al., 2011) having possible impact on

the child’s health status and maternal wellbeing. Due to the complexity of those

processes, routine praxis does not answer many questions. The present thesis

show identification of the molecular processes in the post-delivery placentas of

different gestational ages for the development of clinically applicable

knowledge of molecular processes to create opportunitie11s for the

improvement of perinatal outcome in high risk situations.

Pathways of the pathological processes have been researched by

different methods; immunohistochemistry (IHC) has been found to be sensitive

for the understanding of pathological processes and establishment of accurate

clinical diagnosis in more complicated cases (Takizawa et al., 2007). IHC

research has been used in the cases of preeclampsia and intrauterine growth

restriction (IUGR) (Shen et al., 2011; Cayli et al., 2012; Cozzi et al., 2012),

non-immune fetal hydrops (Bellini et al., 2010). IHC reveals molecular

processes in the placental tissues and possibly could disclose specific targets

of clinical management, improving efficiency of the treatment and outcome.

A drawback of a practical application of an IHC research is its specificity

(Ramos-Vara, 2005); for this reason it would be very useful to identify a scope

of reliable markers of the molecular processes in placenta, imperceptible in

a routine examination, but possibly having led to one or another outcome.

We were interested in several kinds of placental markers that we

expected to be involved in the development of fetus and his environment:

growth factors, cytokines, proteins of the basement membrane, tissue degrading

8

enzymes as well as products of the homeobox genes; were assessed average

numbers of apoptotic cells per visual field. From the growth factors and their

receptors were chosen the following: one of the most potent growth factors

Insulin-like growth factor 1 (IGF1) (Kansra et al., 2012; Becker et al., 2012)

and its receptor IGFR1, important factors of fetal growth and promoters of

placental development and function (Sferruzzi-Perri et al., 2007; Forbes and

Westwood, 2008; Sferruzzi-Perri et al., 2008; Sferruzzi-Perri et al., 2008),

Hepatocyte growth factor (HGF), playing a significant role in the development

of the placenta (Uehara et al., 1995) and fetus (Bladt et al., 1995; Schmidt et al.,

1995; Ebens et al., 1996), Fibroblast growth factor basic (bFGF) and its

receptor FGFR1, having been found to be increased in placentas in cases of

pathological pregnancies (Hill et al., 1998; Arany et al., 1998; Ozkan et al.,

2008). For the research were chosen pro-inflammatory cytokines IL-1α and

TNFα as well as anti-inflammatory cytokine IL-10, expecting impact of their

interaction and balance on the course of pregnancy (Xie et al., 2011; Chabtini et

al., 2012; Chaparro et al., 2012; Kim et al., 2012; Lamarca et al., 2012; Parveen

et al., 2012; Taki et al., 2012; Twig et al., 2012; Wu et al., 2012) and its

aoutcome (Mitchel et al., 2004; Kobayashi et al., 2010; Brogin Moreli et al.,

2012). Collagen IV, one of the constituents of the basement membrane,

determinant of the strength of tissues (Kühn, 1995; Nerlich, 1995), participates

in the formation of placental barrier (Mori et al., 2007; Jones et al., 2008) and is

altered in pathological pregnancies (Hu et al., 2004; van der Velde et al., 1985;

Asfhaq et al., 2003; Rath et al., 2011), leading to placental insufficiency

(Khozhaĭ et al., 2010). Degradation of extracellular matrix (ECM) is initiated

by various enzymes, the most significant of whom are matrix

metalloproteinases (MMP) (Reynolds, 1996; Shingleton et al., 1996; Xu et al.,

2002; Cawston and Young, 2010); Collagen IV is degraded by MMP2 and

MMP9, having impact on the tissue integrity. Coherence of remodeling

9

processes of ECM can determine the course and outcome of pregnancy (Hopper

et al., 2003). Although MMP9 has already been acknowledged to be a signi-

ficant player in pregnancy, all the questions have not been answered yet.

Development of humen embryo is influenced by a scope of homeobox genes,

the most investigated of them are Hox genes. They were found to determine

growth of anterior-posterior axis of Drosophila melanogaster; further studies

confirmed their role in the development of mammal brain (Mc Ginnis and

Krumlauf, 1992; Krumlauf, 1994; Schneider-Maunoury et al., 1998; Carapuço

et al., 2005; Tümpel et al., 2009) and hemopoesis (Lawrence et al., 1997). Hox

gene products – proteins act as trascription factors, influencing transcription of

Ribonucleic acid (RNA). Apoptosis, programmed cell death, plays a significant

role in morphogenesis and development of placenta (Smith et al., 1997;

Huppertz et al., 1998; Mayhew et al., 1999; Mayhew, 2001; Hupperz et al.,

2004), promoting growth and development of villi. In pathological cases

apoptosis is related with fetal growth restriction (Smith et al., 1997; Axt et al.,

1999; Erel et al., 2001; Liu et al., 2002; Burton et al., 2009) or preeclampsia

(Huppertz et al., 2003; Kadyrov et al., 2006; Chen et al., 2010; Sharp et al.,

2010; Chamley et al., 2011). Assessment of cellular apoptosis in the placental

tissues can provide useful insight into its processes.

Research of the presence of mentioned markers in placentas of various

gestational ages can improve knowledge regarding gestation related interaction

between those processes and their impact on the course and outcome of

pregnancy.

10

2. AIM OF THE STUDY

The aim of the study was to research the main molecular events of

cellular growth, regeneration, cell death, tissue degradation, inflammation and

gene expression in pre-term and term placentas of different gestational ages,

pregnancy risk factors, as well as some anthropometrical and clinical indices of

mothers and newborns for the identification of the most important diagnostic

and prognostic factors of placental status and fetal well-being.

11

3. STUDY TASKS

For achievement of the aim were defined the following tasks:

1. To provide statistical analysis of anthropological and basic clinical

data of the study patients (women and babies);

2. To assess appearance and distribution of the following molecular

factors in placentas of various gestational ages: IGF1, IGFR1, HGF,

bFGF, FGFR1, IL-1α, TNFα, IL-10, Collagen IV, MMP9;

3. To evaluate amount and distribution of apoptotic cells in the tissues

of placentas of various gestational ages;

4. To assess appearance and distribution of the products of homeobox

gene HoxB3 in placentas of various gestational ages;

5. To look for correlations between the factors of molecular processes

in the placentas and gestational age, anthropometrical parameters

and main clinical indices of the mothers and newborns.

12

4. NOVELTY OF THE STUDY

The study researched appearance of different molecular factors in

placentas of various gestational ages in situ and looked for correlations of the

factor positive cells with gestational age, anthropological parameters of the

mother and neonate as well as main clinical findings; similar studies have not

been described in the literature and can provide information regarding possible

impact of molecular processes on the course and outcome of pregnancy.

13

5. MATERIALS AND METHODS

On the 12th of March, 2009 the study was approved by the Ethics

Committee of RSU. 53 HIV negative patients of legal age without systemic

diseases, having received sufficient antenatal care and admitted for the delivery

care in the Riga Maternity hospital, signed informed consent and were included

in the study.

Study groups:

• Group 1 (term): 14 term deliveries with a healthy child from 37 till

41 weeks of pregnancy;

• Group 2 (pre-term): 25 pre-term deliveries with a pre-term child

from 22 till 36 weeks of pregnancy;

• Group 3 (distress): 14 deliveries of various gestational ages with an

episode of a clinically significant fetal distress before or during the

delivery.

Two samples of 1 x 1 cm, taken through all the layers of placenta, were

put into fixation of Picric Acid-Formaldehyde (Stefanini et al., 1967) and

delivered to the Institute of Anatomy and Anthropology of RSU for further

processing. Maternal, placental and neonatal data were obtained from the

medical records of Childbirth and Neonatal development of the Riga Maternity

hospital and the study survey. Samples underwent the following processing:

• Routine staining with hematoxylin and eosin in accordance with

H&E Staining Method and Protocol (Avwioro, 2011;

www.ihcworld.com);

• Immunohistochemical (IHC) staining with chosen antibodies (Table

5.1.). Preparation of the samples for IHC processing with mouse

and rabbit antibodies was provided in accordance with the Dako

REALTM EnVision Detection System protocol (3rd edition, 2005).

14

In the cases, where goat antibodies were used, processing was

provided in correspondence with the ImmunoCruz goat ABC

Staining System protocol sc-2023 (Santa Cruz Biotechnology, Inc.,

2011).

• TUNEL was processed by the means of standard In Situ Cell Death

Detection kit, POD Cat. No 11684817910, manufactured by Roche

Diagnostics (Negoescu et al., 1998).

• Negative and positive controls were provided.

Table 5.1. Used IHC antibodies

Antibody Clone Species Company Dilution 1. IGF1 56408 mouse R&D 1 : 50 2. IGFR1 polyclonal goat R&D 1 : 100 3. HGF polyclonal goat R&D 1 : 300 4. FGFb polyclonal rabbit Abcam 1 : 200 5. FGFR1 polyclonal rabbit Abcam 1 : 100 6. IL-10 polyclonal rabbit Abcam 1 : 400 7. IL-1α B-7 mouse Santa Cruz 1 : 50 8. TNFα polyclonal rabbit Abcam 1 : 100 9. Caspase EP13254 rabbit Abcam 1 : 200

10. Collagen IV CIV94 mouse Invitrogen 1 : 30 11. MMP9 polyclonal rabbit Santa Cruz 1 : 250 12. HoxB3 polyclonal rabbit Santa Cruz 1 : 100

IHC findings were evaluated semi–quantitatively by the amount of the

indicator positive cells or ECM structures in a visual field (Pilmane et al.,

1998): none 0, occasional 0/+, few +, moderate ++, numerous +++ and

abundant ++++. Evaluation was done after complete observation of both

samples of each placenta. IHC findings were ranked in the ascending order by

modified competition ranking method (Pozzi, 2008): correspondingly 0; 0.5; 1;

2; 3; 4; apoptotic cells were counted in 10 visual fields. Evaluations were

provided in the following sections:

1. In the whole study, including 53 deliveries;

15

2. In three study groups: 14 patients term (G1), 25 patients pre-term

(G2) and 14 term or pre-term fetal distress patients (G3);

3. In gestation dependent groups: 19 term and 34 pre-term patients;

4. In specific patient groups, determined by the expected impact of the

factor.

Descriptive statistics for the whole study group and selected study

groups were performed. For cross-sample mean comparison, inferential

statistical Student's t-tests were performed. This method was chosen due to the

approximate normality of the data analyzed.

Pearson product-moment correlation was used to inspect the correlation

of mother and neonate specific data with their respective indicators, as well as

between the indicators themselves. This measure was chosen due to the linear

relationships observed between the correlated data, the strong tendency of the

mother, neonate and indicator data to follow normal distribution, as well as the

homoscedastic nature of the data analyzed. Pearson correlation and Mann-

Whitney U Test were considered and tested for correlation analysis, and

Pearson correlation was chosen due to the normal distribution of the data

analyzed.

Data processing software Microsoft Excel 2013 Preview and IBM SPSS

19.0 were used; the statistical significance was set at p < 0.05.

16

6. RESULTS

6.1. Maternal parameters

The study included 53 delivery patients with gestational time at delivery

from 22 till 40 weeks; 30 (57%) were vaginal and 23 (43%) were by Cesarean

section (CS), 9 of them (20%) were emergency CS due to fetal distress;

maternal anthropological parameters are shown in the Table 6.1.

Maternal age presented statistically significant positive correlations with

the numbers of pregnancies and deliveries (Table 6.2.).

Table 6.2. Correlations between maternal parameters

Maternal age Pregnancies Deliveries

Maternal age Pearson Corr. 1 0.527** 0.425** Sig. (2-tailed) 0.000 0.002

Pregnancies Pearson Corr. 0.527** 1 0.733** Sig. (2-tailed) 0.000 0.000

Deliveries Pearson Corr. 0.425** 0.733** 1 Sig. (2-tailed) 0.002 0.000

** Statistically significant strong correlation (p < 0.01)

Maternal parameters differed between the study groups G1 (term) and

G2 (pre-term); mean value of the number of pregnancies was statistically

significantly higher in the study group G2 (pre-term) than in the group G1

Table 6.1. Maternal data

Min Max Mean ± SD Maternal age 18 39 29.79 ± 5.6 Number of pregnancies 1 7 2.59 ± 1.64 Number of deliveries 1 6 1.75 ± 0.98 Weeks of pregnancy 22 40 33.02 ± 5.18 Body mass before actual pregnancy (kg) 46 109 65.82 ± 12.75 Body height (cm) 150 182 166.33 ± 7.025 BMI prior to pregnancy (kg/m2) 17.20 42.60 23.79 ± 4.74 Increase of the body mass in pregnancy (kg) 0 26 10.90 ± 5.81

17

(term) (Table 6.3.); mean time of ruptured membranes (ROM) also was longer

in the study group G2 (pre-term).

Table 6.3.

Differences of the maternal parameters between the study groups G1 and G2

Mean SD Std. Error Mean

Sig. (t-test for Equality of

Means)

Pregnancy G1 (term) 1.86 0.949 0.254 0.022* G2 (pre-term) 2.91 1.730 0.361 Length of ROM

G1 (term) 4.29 4.687 1.253 0.012* G2 (pre-term) 52.27 84.392 17.597 * Correlation is significant at the 0.05 level

6.2. Neonatal parameters

The mean gestational age of neonates was 33.06 ± 5.09 weeks: 34 were

premature, born in 22-35 weeks of pregnancy; 19 were mature, born in 37-40

weeks of pregnancy; anthropomtrical data are shown in the Table 6.4.

Table 6.4. Neonatal anthropometric parameters

Minimum Maximum Mean ± SD Birth weight 540 4630 2367.15 ± 1122.59 Length 28 59 45.51 ± 7.433 Ponderal index 1.74 3.13 2.33 ± 0.31 Head 22 39 31.15 ± 4.34 Chest 20 37 29.19 ± 4.93

39 of the neonates were appropriate for the gestational age (AGA),

weight of 5 neonates was too small for the gestational age (SGA) and 9 were

large for the gestational age (LGA). 4 of SGA and 3 of LGA neonates were

premature.

In the whole sample anthropometric parameters: body weight, body

length, head and chest circumference were directly proportional to the

gestational age and between themselves (Table 6.5.).

18

Table 6.5. Correlation of the anthropometric parameters of the neonates

Gestation

Weight Length Head Chest

Gestation Pearson corr. 1 0.906** 0.900** 0.887** 0.905** Sig. (2-tailed) 0.000 0.000 0.000 0.000

Weight Pearson corr. 0.906** 1 0.938** 0.936** 0.961** Sig. (2-tailed) 0.000 0.000 0.000 0.000

Length Pearson corr. 0.900** 0.938** 1 0.936** 0.934** Sig. (2-tailed) 0.000 0.000 0.000 0.000

Head Pearson corr. 0.887** 0.936** 0.936** 1 0.951** Sig. (2-tailed) 0.000 0.000 0.000 0.000

Chest Pearson corr. 0.905** 0.961** 0.934** 0.951** 1 Sig. (2-tailed) 0.000 0.000 0.000 0.000

** Correlation is significant at the 0.01 level (2-tailed)

Criteria for inclusion in the study groups determined statistically

significant differences of neonatal anthropometrical parameters (Table 6.6.).

Table 6.6.

Differences between neonatal anthropometrical parameters in the study

groups

Parameter Group Mean SD Std. Error Mean p*

Body mass

G1 (term) 3591.43 503.019 134.438 0.000** G2 (pre-term) 1721.28 578.325 115.665 G1 (term) 3591.43 503.019 134.438 0.002** G3 (distress) 2296.21 1322.32 353.406

Body length


Head circumference


Chest circumference


* Sig. (t-test for Equality of Means) ** Statistically significant strong correlation (p < 0.01)

19

Study group G1 (term) included 14 term deliveries in 37-40 weeks of

pregnancy with healthy children (7 girls; 7 boys) with body mass from 2740 g

till 4410 g (Table 6.7.).

Table 6.7.

Study group G1 (term)

Mat

erna

age

(y

ears

)

Preg

nacy

(n

umbe

r)

Del

iver

y (n

umbe

r)

Mat

erna

l BM

I (k

g/m

2 ) bef

ore

preg

nanc

y

Wee

ks o

f pr

egna

ncy

Gen

der o

f the

ne

onat

e

Neo

nata

l bod

y m

ass (

g)

Neo

nata

l bod

y le

ngth

(cm

)

1’ A

pgar

scor

e

5’ A

pgar

scor

e

1. 27 1 1 42.6 37 G* 3020 49 8 8 2. 37 3 3 20.4 37 G 3200 50 7 9 3. 25 2 2 23.8 37 G 3270 50 7 8 4. 27 2 2 26.7 38 B** 4290 57 8 9 5. 28 2 2 26.4 39 G 2740 50 7 8 6. 39 1 1 20.4 39 B 3230 53 8 9 7. 23 1 1 25.5 39 G 3540 52 7 8 8. 19 2 1 21.9 39 B 3670 52 8 9 9. 32 2 1 22.9 39 G 3810 55 8 9

10. 30 3 3 20.3 39 B 4000 54 8 9 11. 25 1 1 26.0 40 B 3250 54 7 8 12. 21 1 1 20.9 40 G 3670 54 6 8 13. 21 1 1 20.4 40 B 4180 55 8 9 14. 37 4 2 30.5 39 B 4410 56 7 8

* G – girl ** B – boy

Study group G2 (pre-term) included 25 pre-term deliveries in 22-35

weeks of gestation with liveborn premature neonates (16 girls; 9 boys) with

body mass from 540 g till 2580 g (Table 6.8.).

20

Table 6.8. Study group G2 (pre-term)

M

ater

na a

ge

(yea

rs)

Preg

nacy

(n

umbe

r)

Del

iver

y (n

umbe

r)

Mat

erna

l BM

I (k

g/m

2 ) bef

ore

preg

nanc

y

Wee

ks o

f pr

egna

ncy

Gen

der o

f the

ne

onat

e

Neo

nata

l bod

y m

ass (

g)

Neo

nata

l bod

y le

ngth

(cm

)

1’ A

pgar

scor

e

5’ A

pgar

scor

e

1. 20 1 1 24.6 22 G* 540 30 6 6 2. 36 2 2 21.0 23 Z** 650 28 1 2 3. 30 4 2 20.7 24 G 720 32 3 5 4. 18 1 1 28.0 28 G 1130 36 4 6 5. 34 6 3 32.3 28 G 1190 38 6 7 6. 34 1 1 21.0 28 G 1290 37 7 8 7. 37 2 2 25.6 28 G 1410 41 7 7 8. 34 2 2 23.8 29 B 1476 39 7 7 9. 32 4 4 22.9 30 G 1580 41 5 7

10. 36 2 2 22.9 30 B 1710 42 7 8 11. 25 2 2 19.7 30 B 1750 44 6 7 12. 29 4 1 19.9 31 G 1370 38 7 7 13. 28 4 1 20.0 31 G 1780 45 7 7 14. 34 2 2 19.4 31 B 1888 40 7 7 15. 35 1 1 36.6 31 B 2060 46 7 8 16. 23 1 1 19.6 32 B 1940 46 7 7 17. 30 7 6 24.4 32 G 2258 49 7 7 18. 35 4 3 26.8 33 G 1948 46 7 7 19. 21 1 1 18.7 33 G 1992 45 7 7 20. 37 4 2 29.8 33 B 2300 45 7 8 21. 27 2 1 18.6 34 G 2310 48 7 8 22. 29 3 2 26.6 34 G 2320 42 7 8 23. 29 3 2 26.6 34 G 2390 47 7 7 24. 27 2 1 19.9 34 B 2450 48 7 8 25. 29 2 1 30.1 34 G 2580 44 7 7

* G – girl ** B – boy

Study group G3 (distress) included 14 term and pre-term deliveries in

23-40 weeks of pregnancy with liveborn or stillborn neonates (7 girls; 7 boys)

with body mass from 905 g till 4630 g (Table 6.9.).

21

Table 6.9. Study group G3 (distress)

M

ater

na a

ge

(yea

rs)

Preg

nacy

(n

umbe

r)

Del

iver

y (n

umbe

r)

Mat

erna

l BM

I (k

g/m

2 ) bef

ore

preg

nanc

y

Wee

ks o

f pr

egna

ncy

Gen

der o

f the

ne

onat

e

Neo

nata

l bod

y m

ass (

g)

Neo

nata

l bod

y le

ngth

(cm

)

1’ A

pgar

scor

e

5’ A

pgar

scor

e

1. 28 1 1 19.9 23 B* 905 34 0 0 2. 32 6 4 22.9 28 B 1090 35 6 7 3. 36 2 2 21.0 28 G** 1360 41 6 7 4. 25 1 1 19.7 29 G 1114 40 6 7 5. 28 4 1 19.9 30 B 1860 41 7 7 6. 34 6 2 19.4 31 B 1896 44 7 8 7. 36 2 2 22.9 32 G 1088 38 7 7 8. 35 2 2 36.6 33 G 1888 43 7 7 9. 34 1 1 23.8 35 B 1920 45 7 7

10. 37 3 3 25.6 37 G 2770 49 0 0 11. 23 5 2 19.6 38 G 2786 54 0 0 12. 34 4 3 21.0 38 B 4510 55 0 2 13. 21 1 1 18.7 40 B 4330 56 1 2 14. 18 3 1 28.0 40 G 4630 59 0 0

* B – boy ** G – girl

6.3. Placental parameters

Placental weight was 220 g – 930 g with a mean value of 448.95 ±

157.503 g, mean values of the placental weight in the study groups are shown

in the Table 6.10.

Table 6.10. Placental weight in the study groups

Study group Range (g) Mean (g) G1 (terms) 480-750 663.33 ± 102.632 G2 (pre-term) 220-510 421.10 ± 189.968 G3 (distress) 240-930 516.07 ± 216.157

Placental weights of the study group G2 (pre-term) was significantly

lower than those one of the study group G1 (term) (Table 6.11.). Similar

difference was found between term and pre-term placentas of the whole study.

22

Table 6.11. Differences between placental weight in the study groups

Mean SD Std. Error Mean p*

Placental weight

G1 (term) 663.33 102.63 59.255 0.007** G2 (pre-term) 403.39 145.39 30.432 * Sig. (t-test for Equality of Means) ** Statistically significant strong correlation (p < 0.01)

In the whole study appeared statistcally significant positive correlations

between the placentas weight and the following maternal parameters (Table

6.12.): body mass before pregnancy, gestation (also in the study group G2 pre-

term) as well as increase of the body mass during pregnancy (also in term cases

of the whole study).

Table 6.12. Correlations of the placental mass with maternal parameters

Maternal parameter Placental mass

Gestation Pearson corr. 0.537** Sig. (2-tailed) 0.001

Body mass before pregnancy Pearson corr. 0.342* Sig. (2-tailed) 0.036

Increase of body mass in pregnancy Pearson corr. 0.342* Sig. (2-tailed) 0.036

* Statistically significant correlation (p < 0.05) ** Statistically significant strong correlation (p < 0.01)

In the whole study placental masses presented statistically significant

positive correlation with all the main anthropometrical parameters of the

neonates (Table 6.13.).

Table 6.13. Correlations of the placental mass with neonatal parameters

Neonatal parameter Placental mass All study and study group G2 (pre-term)

Body mass Pearson corr. 0.749** Sig. (2-tailed) 0.000

Body length Pearson corr. 0.692** Sig. (2-tailed) 0.044

Tabele 6.13. continued p.23

23

Table 6.13. (End) Neonatal parameter Placental mass

Head circumference Pearson corr. 0.725** Sig. (2-tailed) 0.000

Chest circumference Pearson corr. 0.788* Sig. (2-tailed) 0.000

Study group G1 (term)

Chest circumference Pearson corr. 0.801* Sig. (2-tailed) 0.030

Ponderal index Pearson corr. - 0.975** Sig. (2-tailed) 0.001

Study group G3 (distress)

Ponderal index Pearson corr. 0.554* Sig. (2-tailed) 0.040

* Statistically significant correlation (p < 0.05) ** Statistically significant strong correlation (p < 0.01)

Similar correlations were seen in the study group G2 (pre-term) and pre-

term placentas of the whole study; in the study group G1 (term) appeared

positive correlations of the placentas messes with neonatal chest

circumferences and negative with their ponderal indices. In the study group G3

(distress) appeared a positive correlation between placentasl masses ans

ponderal indices of neonates.

6.4. Routine microscopy of placental preparations

Staining with hematoxylin and eosin (HE) revealed differences in

morphological maturity of placental preparations: second trimester placentas

were young (Figure 6.1.), placentas of early third trimester transitional (Figure

6.2.) and those of late third trimester presented signs of ageing (Figure 6.3.).

24

Figure 6.1. Maternal part and tertiary villi of a young 23 weeks placenta HE, X 250

Figure 6.2. Maternal part and teriary villi of a transitional 28 weeks placenta HE, X 250

Figure 6.3. Maternal part and tertiary villi of a 40 weeks placenta; signs of ageing HE, X 250

25

6.5. IHC research of placental samples

Growth factors and receptors: IGF1, IGFR1, HGF, bFGF un FGFR1

Amount of IGF1 positive cells in the placental tissues did not correlate

with their gestational ages and were found from 0 (none) till abundance (++++)

in a visual field (Figures 6.4. and 6.5.). By the means of the modified

competition ranking method (Pozzi, 2008) were estimated ranks with further

calculation of the mean rank values of the findings (Table 6.14.).

Table 6.14.

The rank values of IGF1 positive cells in different divisions of the study

Group Mean ± SD 1. The whole study 1.58 ± 0.86 2. Study group G1 (healthy term) 1.32 ± 0.75 3. Study group G2 (normal pre-term) 1.74 ± 0.99 4. Study group G3 (distress) 1.54 ± 0.69 5. Term placentas of the whole study 1.40 ± 0.70 6. Pre-term placentas of the whole study 1.68 ± 0.94

Figure 6.4. Few (+) IGF1 positive cells in a 40 gestational weeks placenta

IGF1 IHC, X 250

Figure 6.5. Abundance (++++) of IGF1 positive cells in a 22 weeks

placenta IGF1 IHC, X 250

Amount of IGFR1 positive cells significantly decreased with ongoing

pregnancy; they were in almost all placentas from 0 (none) till abundance

26

(++++) (Figures 6.6. and 6.7.). By the means of the modified competition

ranking method (Pozzi, 2008) were estimated ranks with further calculation of

the mean rank values of the findings (Table 6.15.).

Figure 6.6. Occasional (0/+) IGFR1 positive cells in a 23 weeks placenta

IGFR1 IHC, X 250

Figure 6.7. Numerous (+++) IGFR1 positive cells in a 40 weeks placenta

IGFR1 IHC, X 250

Table 6.15.

The rank values of IGFR1 positive cells in different divisions of the study


In pre-term placentas appeared statistically significant higher rank

values of IGFR1 positive cells in comparison with term placentas (Table 6.16.).

Table 6.16.

Differences of the rank values of IGFR1 positive cells in the study groups

Mean SD Std. Error Mean p* G1 (term) 1.250 0.8263 0.2208 0.002** G2 (pre-term) 2.380 1.2440 0.2488 G1 (term) 1.250 0.8263 0.2208 0.002** G3 (distress) 2.286 0.7263 0.1941 Tabele 6.16. continued p.27

27

Table 6.16. (End) Mean SD Std. Error Mean p* Term of the whole study 1.553 0.9113 0.2091 0.009** Pre-term of the whole study 2.338 1.1396 0.1954


Appeared a statistically significant difference between placentas of SGA

and LGA neonates (Table 6.17.).

Table 6.17.

Differences between rank values of IGFR1 positive cells in AGA and LGA placentas

Parameter N Mean SD Std. Error Mean p*

IGFR1 SGA 5 16.00 5.477 2.449 0.024* LGA 9 26.67 10.00 3.333 * Sig. (t-test for Equality of Means) ** Statistically significant correlation (p < 0.05)

Amount of HGF positive cells in placentas did not correlate with their

gestational ages and were from 0 (none) till abundance (++++) in a visual field

(Figures 6.8. and 6.9.). By the means of the modified competition ranking

method (Pozzi, 2008) were estimated ranks with further calculation of the mean

rank values of the findings (Table 6.18.).

Table 6.18.

The rank values of HGF positive cells in different divisions of the study


28

Figure 6.8. Moderate amount (++) of HGF positive cells in a 32 weeks

placenta HGF IHC, X 250

Figure 6.9. Abundance (++++) of HGF

positive cells in a 29 weeks placenta HGF IHC, X 250

Basic FGF (bFGF) presented weak immunoreactivity and was visually

identified only in few preparations (Figures 6.10. and 6.11.), therefore was

excluded from further research.

Figure 6.10. Moderate amount (++) of bFGF positive cells in

a 40 weeks placenta bFGF IHC, X 250

Figure 6.11. Occasional (0/+) bFGF positive cells in a 38 weeks placenta

bFGF IHC, X 250

The rank values of FGFR1 positive cells in a visual field of a placenta

did not correlate with its gestational age; they were found in all the placental

samples from occasional (0/+) till abundance (++++) in a visual field (Figures

29

6.12. and 6.13.). By the means of the modified competition ranking method

(Pozzi, 2008) were estimated ranks with further calculation of the mean rank

values of the findings (Table 6.19.).

Table 6.19.

The rank values of GFGR1 positive cells in different divisions of the study


Figure 6.12. Moderate amount (++) FGFR1 positive cells in

a 31 weeks placenta FGFR1 IHC, X 250

Figure 6.13. Numerous (+++)

FGFR1 positive cells in a 40 weeks placenta FGFR1 IHC, X 250

Correlations with anthropological and clinical parameters in the

study group G1 (term) revealed positive correlations of the rank values of HGF

with maternal BMI prior to actual pregnancy and the rank values of FGFR1

showed negative correlations with maternal weight before actual pregnancy and

neonatal glucose level (Table 6.20.). In the study group G2 (pre-term) the rank

values of IGF1 positively correlated with the neonatal glucose and the rank

values of IGFR1 – with the first neonatal blood pH. In the study group G3

30

(distress) the rank values of HGF presented negative correlations with the

number of maternal pregnancies.

Table 6.20.

Correlations of the rank values of growth factors/receptors with anthropological and basic clinical parameters

Parameter IGF1 IGFR1 HGF FGFR1 Study group G1 (term)

Maternal BMI prior to pregnancy

Pearson cor. NS NS 0.601* NS Sig. 0.023 Maternal body mass prior to pregnancy

Pearson cor. NS NS NS -0.644* Sig. 0.017

Neonatal blood glucose

Pearson cor. NS NS NS -0.638* Sig. 0.026

Study group G2 (pre-term)

Neonatal blood pH Pearson cor. NS 0.428* NS NS Sig. 0.033 Neonatal first blood glucose

Pearson cor. 0.444* NS NS NS Sig. 0.026 Study group G3 (distress)

Number of pregnancies

Pearson cor. NS NS -0.538* NS Sig. 0.047 * Statistically significant correlation (p < 0.05) NS – Statistically non-significant correlation

Correlations between the rank values of researched growth factor/

receptor positive cells and other maternal or neonatal parameters were not

statistically significant.

Cytokines: pro-inflammatory IL-1α, TNFα and anti-inflammatory IL-10

Amount of pro-inflammatory cytokine IL-1α positive cells was from 0

(none) till numerous (+++) in a visual field of placental preparation (Figures

6.14. and 6.15.); their amount did not correlate with the gestational age By the

means of the modified competition ranking method (Pozzi, 2008) were

estimated ranks with further calculation of the mean rank values of the findings

(Table 6.21.).

31

Table 6.21. The rank values of IL-1α positive cells in different divisions of the study


Figure 6.14. Few (+) IL-1α positive cells in a 39 weeks placenta

IL-1α IHC, X 250

Figure 6.15. Numerous (+++) IL-1α positive cells in a 31 weeks placenta

IL-1α IHC, X 250

Amount of pro-inflammatory cytokine TNFα positive Höfbauer cells

were from 0 (none) till numerous (+++) in a visual field (Figures 6.16. and

6.17.). By the means of the modified competition ranking method (Pozzi, 2008)

were estimated ranks with further calculation of the mean rank values of the

findings (Table 6.22.).

Table 6.22

The rank values of TNFα positive cells in different divisions of the study


32

Figure 6.16. Few (+) TNFα positive cells in a 37 weeks placenta

TNFα IHC, X 250

Figure 6.17. Numerous (+++) TNFα positive cells in a 34 weeks placenta

TNFα IHC, X 250

Amount of anti-inflammatory cytokine IL-10 positive cells were seen in

all the preparations from few (+) till abundance (++++) in a visual field; their

number decreased with advanced gestation (Figures 6.18. and 6.19.). By the

means of the modified competition ranking method (Pozzi, 2008) were

estimated ranks with further calculation of the mean rank values of the findings

(Table 6.23.).

Table 6.23.

The rank values of IL-10 positive cells in different divisions of the study


Differences between the study groups were not statistically significant;

appeared a statistically significant correlation between the rank values of IL-10

in appropriate for the gestational age (AGA) and large for the gestational age

(LGA) neonate placentas (Table 6.24.).

33

Table 6.24.

Differences between the rank values of IL-10 positive cells in placentas of AGA and LGA neonates

Parametrs N Mean SD Std. Error Mean p*

IL-10 AGA 39 28.72 10.047 1.609 0.024** LGA 9 34.44 5.270 1.757 * Sig. (t-test for Equality of Means) ** Statistically significant correlation (p < 0.05)

Figure 6.18. Moderate amount (++) of IL-10 positive cells in

a 34 weeks placenta IL-10 IHC, X 250

Figure 6.19. Abundance (++++)

of IL-10 positive cells in a 22 weeks placenta

IL-10 IHC, X 250

In the whole study appeared a statistically significant positive

correlation between the rank values of TNFα and IL-10 positive cells; similar

relation was seen also in the study group G1 (term). In term placentas of the

whole study and in the study group G1 (term) appeared a statistically

significant positive correlation between the rank values of both pro-

inflammatory cytokines (TNFα and IL-1α).

In the study groups appeared statistically significant correlations

between the rank values of cytokines and anthropological/ clinical data (Table

6.25.): in the study group G1 (term) were found correlations between the rank

values of a pro-inflammatory cytokine TNFα and maternal age, numbers of

34

pregnancies and deliveries. In the study group G2 (pre-term) rank values of IL-

10 negatively correlated with the gestation, rank values of IL-1α presented

positive correlations with ROM and negative – with initial blood pH and

glucose level. In the study group G3 (distress) the rank values of IL-1α

negatively correlated with gestation and TNFα with neonatal blood pH.

Table 6.25.

Correlations of the rank values of cytokines with maternal and neonatal data

Parameter IL-10 IL-1α TNFα Study group G1 (term)

Maternal age Pearson Correlation NS NS 0.614* Sig. (2-tailed) 0.044

Number of pregnancies

Pearson Correlation NS NS 0.628* Sig. (2-tailed) 0.039

Number of deliveries

Pearson Correlation NS NS 0.622* Sig. (2-tailed) 0.041

Study group G2 (pre-term)

Gestation Pearson Correlation -0.440* NS NS Sig. (2-tailed) 0.028

ROM Pearson Correlation NS 0.536** NS Sig. (2-tailed) 0.008 Neonatal blood pH

Pearson Correlation NS -0.559** NS Sig. (2-tailed) 0.004

1’ APGAR Pearson Correlation NS -0.526** NS Sig. (2-tailed) 0.007 Study group G3 (distress)

Gestation Pearson Correlation NS -0.613* NS Sig. (2-tailed) 0.020 Neonatal blood pH

Pearson Correlation NS NS -0.855** Sig. (2-tailed) 0.002

Neonatal blood glucose

Pearson Correlation NS NS 0.768** Sig. (2-tailed) 0.009

* Statistically significant correlation (p < 0.05) ** Statistically significant strong correlation (p < 0.01) NS – Statistically non-significant correlation

Correlations between the rank values of researched cytokine positive

cells and other maternal or neonatal parameters were not statistically

significant.

35

Number of apoptotic cells in the placental tissues

The mean amount of apoptotic cells in the placental tissues was from

0.91 ± 0.83 till 75.91 ± 14.42 in a visual field, decreasing with advanced

gestation (Figures 6.20. and 6.21.). The mean values of apoptotic cells in

various sections of the study are shown in the Table 6.26.

Table 6.26.

The mean numbers of apoptotic cells in different divisions of the study Group Mean ± SD 1. The whole study 22.15 ± 17.82 2. Study group G1 (healthy term) 11.85 ± 11.718 3. Study group G2 (normal pre-term) 27.16 ± 17.899 4. Study group G3 (distress) 22.79 ± 19.292 5. Term placentas of the whole study 13.00 ± 13.052 6. Pre-term placentas of the whole study 27.00 ± 18.246

Figure 6.20. 8.0 ± 3.32 apoptotic cells in a 34 weeks placenta

Caspase IHC, X 250

Figure 6.21. 43.91 ± 6.76 apoptotic

cells in a 40 weeks placenta TUNEL, X 250

In the pre-term placentas apoptotic cells were significantly more (Table

6.27.).

36

Table 6.27.

Differences between the numbers of apoptotic cells

Mean SD Std. Error Mean p* G1 (term) 11.85 11.718 3.250 0.003** G2 (pre-term) 27.16 17.899 3.580 Term of the whole study 13.00 13.052 3.076 0.003** Pre-term of the whole study 27.00 18.246 3.129


Looking for correlations between the number of apoptotic cells in

placentas and clinical findings appeared significantly higher amount of

apoptotic cells in term placentas of mothers with smaller history of

pregnancies; correlation was valid both in the study group G1 (term) and term

placentas of the whole study (Table 6.28.). Table 6.28.

Correlation of the number of apoptotic cells in placenta with clinical data

Parameter Apoptosis Study group G1 (term)

Number of pregnancies Pearson Correlation -0.586* Sig. (2-tailed) 0.035

** Statistically significant correlation (p < 0.05)

Protein of the basement membrane Collagen IV

Collagen IV positive structures were found in almost all the preparations

from 0 (none) till abundance (++++) in a visual field (Figures 6.22. and 6.23.);

structures were more prominenet in term placentas. By the means of the

modified competition ranking method (Pozzi, 2008) were estimated ranks with

further calculation of the mean rank values of the findings (Table 6.29.).

37

Table 6.29.

The rank values of Collagen IV structures in different divisions of the study


Larger amount of Collagen IV structures in term placentas was

statistically significant (Table 6.30.).

Table 6.30. Differences of Collagen IV structures in term and pre-term placentas

Mean SD Std. Error Mean p* Term of the whole study 2.14 1.28 3.020 0.040* Pre-term of the whole study 1.39 1.16 2.015

* Sig. (t-test for Equality of Means) ** Statistically significant correlation (p < 0.05)

Figure 6.22. Few (+) Collagen IV positive structures in a 23 weeks placenta

Collagen IV IHC, X 250

Figure 6.23. Abundance (++++) of

Collagen IV structures in a 40 weeks placenta

Collagen IV IHC, X 250

Looking for correlations between the amount of Collagen IV positive

structures and clinical findings appeared a statistically significant positive

38

correlation in term placentas of the whole study with maternal number of

pregnancies and a negative correlation in pre-term placentas of the whole study

with maternal weight before the actual pregnancy (Table 6.31.).

Table 6.31. Correlation of Collagen IV rank values with clinical data

Parameter Collagen IV Term placentas of the whole study

Number of pregnancies Pearson Correlation 0.494* Sig. (2-tailed) 0.037

Pre-term placentas of the whole study Maternal body mass prior to pregnancy

Pearson Correlation -0.363* Sig. (2-tailed) 0.038

* Statistically significant correlation (p < 0.05)

Tissue degrading enzyme MMP9

MMP9 positive cells were seen in almost all the placental preparations

from 0 (none) till abundance (++++) in a visual field (Figures 6.24. and 6.25.);

findings did not correlate with the gestational age. By the means of the

modified competition ranking method (Pozzi, 2008) were estimated ranks with

further calculation of the mean rank values of the findings (Table 6.32.). Table 6.32.

The rank values of MMP9 positive cells in different divisions of the study


39

Figure 6.24. Moderate amount (++) of MMP9 positive cells in

a 33 weeks placenta MMP9 IHC, X 250

Figure 6.25. Abundance (++++)

of MMP9 positive cells in a 23 weeks placenta MMP9 IHC, X 250

Comparison of MMP9 rank values in study groups appeared a

statistically significant difference between the study groups G1 (term) and G3

(distress) (Table 6.33.).

Table 6.33.

Differences of MMP9 rank values between the study groups Mean SD Std. Error Mean p* G1 (term) 0.857 0.745 0.199 0.009** G3 (distress) 2.071 1.439 0.384

* Sig. (t-test for Equality of Means) ** Statistically significant correlation (p < 0.05)

Looking for correlations between the rank values of MMP9 positive

cells and clinical data appeared a few statistically significant ones: in the study

group G1 (term) – positive correlations with neonatal body mass and chest

circumference, in the study group G2 (pre-term) a positive correlations with

neonatal blood pH and in the study group G3 (distress) – a negative correlation

with neonatal blood glucose (Table 6.34.).

40

Table 6.34.

Correlations between the rank values of MMP9 and clinical data

Parameter MMP9 Study group G1 (term)

Neonatal body mass Pearson Correlation 0.622* Sig. (2-tailed) 0.018

Chest circumference Pearson Correlation 0.550* Sig. (2-tailed) 0.042 Study group G2 (pre-term)

Neonatal blood pH Pearson Correlation 0.464* Sig. (2-tailed) 0.020 Study group G3 (distress)

Neonatal blood glucose Pearson Correlation -0.713* Sig. (2-tailed) 0.021

* Statistically significant correlation (p < 0.05)

Correlations between the rank value of MMP9 positive cells and other

maternal or neonatal parameters were not statistically significant.

HoxB3 gene products

HoxB3 gene product positive cells were found in all the placental

preparations from occasional (0/+) till numerous (+++) in a visual field

(Figures 6.26. and 6.27.); their amount did not correlate with the gestational

age. By the means of the modified competition ranking method (Pozzi, 2008)

were estimated ranks with further calculation of the mean rank values of the

findings (Table 6.35.).

Table 6.35.

The rank values of HoxB3 product positive cells in different divisions of the study


41

Figure 6.26. Moderate amount (++) of HoxB3 product positive cells in

a 32 weeks placenta HoxB3 IHC, X 250

Figure 6.27. Numerous (+++) HoxB3

product positive cells in a 40 weeks placenta HoxB3 IHC, X 250

Looking for correlations between the rank values of HoxB3 product

positive cells and clinical data appeared a few statistically significant

correlations with longitudinal measurements of maternal and fetal bodies

(Table 6.36.).

Table 6.36. Correlations between the rank values of HoxB3 product positive cells and

anthropological data

Parameter HoxB3 products The whole study

Ponderal index Pearson Correlation -0.323* Sig. (2-tailed) 0.018

G1 (laicīgas)

Neonatal body length Pearson Correlation 0.541* Sig. (2-tailed) 0.046

G3 (distresa)

Maternal body height Pearson Correlation 0.573* Sig. (2-tailed) 0.032

Visas priekšlaicīgu dzemdību placentas

Ponderal index Pearson Correlation -0.461** Sig. (2-tailed) 0.008

* Statistically significant correlation (p < 0,05) ** Statistically significant strong correlation (p < 0,01)

Correlations between the rank value of HoxB3 positive cells and other

maternal or neonatal parameters were not statistically significant.

42

7. DISCUSSION 7.1. Anthropology

Growth factors/ receptors: positive cells in general varied depending

on the getstaional age although statistical significance characterised only

decrease of IGFR1 positive cells with advanced gestation. Our data suggested

more significant role of the placental growth factors IGF1 and HGF before the

term, having direct impact on the placental growth, therefore influencing fetal

development. In the literature there are described some studies correlating

gestation related expression of IGF1 and having found its decrease with

ongoing pregnancy (Kumar et al., 2006; Kumar et al., 2012), unchanged (Han

et al., 1996) or even increase in the maternal blood serum (Tennekoon et al.,

2007); neonatal blood also has shown higher level of IGF1 in older gestational

age (Smith et al., 1997). Our findings suggest not just larger amount of IGF1

and IGFR1 positive cells in pre-term placentas, but their impact on viability and

health status of a premature child. In the placentas of our studu group G2 (pre-

term) as well as the pre-term placentas of the whole study the rank of IGFR1

positively correlated with the initial blood pH of the neonate, one of the basic

witnesses of fetal homeostasis; similar correlations in the literature have not

been described. Findings of positive correlations of the circulating level of

IGF1 with the growth velocity of very low birth weight (VLBW) infants during

9 weeks of postnatal life (Kajantie et al., 2003) somehow coincide with our

results showing positive correlations of the rank values of IGF1 in the placentas

of the study group G2 (pre-term). Such a corelation was not found in all the

pre-term placentas of the study (including distress ones) therefore we could

suggest that a larger number of IGF1 positive cells can testify potentially more

promissing health status of a premature neonate and therapeutical application of

IGF1 in the treatment of extremely premature neonates is worth considering.

43

Although decrease of the rank value of HGF with advancing gestational age

showed only weak significance (0.05 ≤ p ≤ 0.1), statistically significant positive

correlations with the rank value of IGFR1 supported this tendency; other

studies describe more prominent expression of HGF in placentas of the 2nd

trimester in comparison with placentas of the 1st trimester (Somerset et al.,

2000) or its decrease in the amniotic membranes with an advanced gestation

and older maternal age (Lopez – Valladares et al., 2010). In our study the rank

values of HGF did not correlate with the maternal age, but presented negative

correlation with the numbers of pregnancies of the mothers, suggesting

decreased capabilities of the maternal organism like it could be suggested in

both conditions. In the study group G1 (term) the rank value of HGF positive

cells in the placentas showed positive correlation with the maternal BMI before

actual pregnancy, to some extent matching with studies, describing HGF as a

driving force of compensatory hyperinsulinemia in obese or type 2 diabetes

patients (Araújo et al., 2012) or pregnant women, who develop hyperglycemia

due to pregnancy induced maternal insulin resistance (Ernst et al., 2011). We

did not find correlations of the rank values of HGF with the blood glucose

levels of the neonates; the described hyperinsulinemia was possibly induced by

HGF in the maternal and not neonatal pancreas. FGFR1 was the only factor of

our researched growth factors and receptors, whose rank value in the term

placentas was higher than in the pre-term ones coinciding with studies, having

stated FGF and FGFR strongly contributing to the growth and development of

the placenta and fetus (Marzioni et al., 2005), pronounced at the end of the third

trimester.

Cytokines: most of the studies researching pro-inflammatory cytokines

in the maternal-fetal unit are focusing on the pre-term parturition and pre-term

premature rupture of membranes (PPROM) (Huleihel et al., 2004; Holcberg et

al.. 2008) and we expected high amount of IL-1α and TNFα positive cells in

44

extremely pre-term placentas; our assumption was not supported, suggesting

non-infectious causes of pre-term deliveries in our cases. Only in the study

group G3 (distress) rank values of placental IL-1α showed negative correlation

with the gestational age, coinciding with studies, offering detection of IL-1 for

an indicator of inflammation and termination of pregnancy due to threat to fetal

health (Girard et al., 2010; Girard et al., 2012). In pre-term placentas we found

significant reduction of the rank value of an anti-inflammatory cytokine IL-10

with advanced gestation, supported by negative correlations with

anthropometrical parameters of the neonates and suggesting decrease of

actuality of maternal immunological tolerance against fetus with advanced

gestation; other studies have not related IL-10 in placental tissues with

gestation (Dembinski et al., 2003).

Apoptosis: programmed cellular death is a process that is present in

placenta throughout the pregnancy, providing turnover of its cells. We found

more apoptotic cells in the pre-term placentas of the study group G2 (healthy

pre-term) in comparison with the term ones in the study group G1 (healthy

term). Our data were somehow controversial with other studies, having

described increased cellular apoptosis with the course of pregnancy (Smith et

al., 1997); their suggestions were based on a comparison of apoptotic cells in

the third trimester with the first trimester while we researched placentas from

the end of the second trimester till term; līdz iznēsātībai; possibly in full-term

placentas changing the balance between apoptotic and necrotic paths of cellular

death is changing towards necrosis (Huppertz et al., 2003; Huppertz et al.,

2004). Additionally in our study numbers of apoptotic cells positively

correlated with HGF rank values, suggesting regenerative capacity of those

placentas.

Collagen IV: amount of this basement membrane constituent positive

structures in placental tissues increased with gestational age, matching with

45

suggestions in the literature regarding indicative role of Collagen IV in fetal

maturation (Papadopuolos et al., 2001); in our study group G3 (distress) the

rank values of Collagen IV positive structures showed positive correlations

with neonatal body mass and chest circumference, suggesting undetected

prolongation of pregnancy as the promoter of fetal distress. Our results showing

increased amount of Collagen IV positive structures in placentas of women

with more pregnancies somehow coincide with findings in the other studies,

having described more structures of Collagen IV in placentas of less favourable

courses of pregnancies: inflammation (Kumar et al., 2006) or hypoxia (Chen

and Aplin, 2003; Chen et al., 2005), including due to maternal smoking (Jalali

et al., 2010).

Tissue degrading enzyme MMP9 in our study did not show significant

correlations with the gestational age, mode of delivery or time of ROM; other

studies describe more prominent expression of MMP9 in cases of pre-term

spontaneous vaginal deliveries (Xu et al., 2002; Sundrani et al., 2012). We

suggest manifestation of MMP9 in maternal-fetal unit with expression in

certain placental cells, coinciding with some other studies (Demir-Weusten et

al., 2007). In our study group G1 (term) the rank values of placental MMP9

positively correlated with neonatal anthropometrical values, suggesting role of

MMP9 in the initiation of term delivery.

7.2. Appropriateness for the gestation

In our study we found a significantly higher mean rank value of IGFR1

in LGA placentas in comparison with SGA ones; finding coincides with

studies, having found higher expression of IGFR1 in the placentas of

macrosomic neonates (Jiang et al., 2009) or higher concentration of IGF1 in the

cord blood of LGA neonates (Akcakus et al., 2006) and also with the studies

46

having found lower expression of IGF1 in the placentas (Regnault et al., 2005;

Koutsaki et al., 2010) or in the blood serum (Orbak et al.,2001; Akcakus et al.,

2006; Lee et al., 2010) of SGA neonates. In the literature have been described

opposite results with higher IGF1 expression in the placentas of SGA neonates

in comparison with the placentas of AGA or LGA ones (Iniguez et al., 2010;

Ahram et al., 2011) as well as negative correlations of IGF1 in the cord blood

with the birth weight (Pathmaperuma et al., 2007). IGF1 and IGFR1 both in our

study and majority of literature sources play a significant positive role in fetal

growth. Placentas of LGA neonates presented significantly higher rank values

of IL-10 than placentas of SGA or AGA neonates; this finding agrees with

studies having found lower expression of IL-10 in placentas of SGA neonates

(Hahn-Zoric et al., 2002); other authors propose deficiency of IL-10 to be

causative for IUGR (Raghupathy et al., 2012). Although the other studies have

found increase of apoptotic cells in placentas of SGA neonates (Athapathu et

al., 2003; Vogt Isaksen et al., 2004; Roje et al., 2011; Longtine et al., 2012), we

did not find such correlations; negative correlations between the numbers of

apoptotic cells in placental tissues with neonatal anthropometrical parameters in

our study is probably associated with gestation.

7.3 Morphology

Our study disclosed several statistically significant correlations between

the rank values of different factors. Rank values of IGF1 and IGFR1 in pre-

term placentas showed positive correlations with the rank values of IL-10

suggesting the role of maternal immune tolerance not just in fetal allograft, but

in fetal growth and development. Such correlation was not found in term

placentas, suggesting decrease of the significance of the maternal immune

tolerance at term; in literature similar correlations have not been described. In

47

our study group G2 (pre-term) rank values of IGF1 presented negative

correlations with the rank values of pro-inflammatory cytokine IL-1α, to a

certain extent supporting described negative impact of pro-inflammatory

cytokines on IGF axis (Hsiao et al., 2011), while the rank values of another pro-

inflammatory cytokine of our study TNFα positively correlated in pre-term

placentas with the rank values of IGFR1 and in term placentas with the rank

values of IGF1, suggesting specific impact of placental pro-inflammatory

cytokines on fetal growth. In the literature there is described increased level of

TNFα in children with growth hormone deficiency without correlations with

level of IGF1 (Andiran and Yordam, 2007); findings are somehow

controversial to our results. In pre-term placentas appeared statistically

significant positive correlations of the rank values of HGF with the rank values

of TNFα and the mean amount of apoptotic cells in a visual field, somehow

coinciding with the role of HGF in tissue regeneration (Matsumoto et al., 2001;

Nayeri et al., 2002; Urbanek et al., 2005; Mizuno et al., 2008; Dai et al., 2010)

and influence on inflammatory processes (Nakamura et al., 2011). Our findings

do not match to studies, having found HGF to inhibit apoptosis in pathological

pregnancies (Dash et al., 2005). Regenerative function of HGF in our study is

supported by positive correlations of its rank values with the rank values of

Collagen IV structures in term placentas; lack of HGF probably indicates

decompensation of placental regenerative capabilities. One of pregnancy

success factors is balance of pro- and anti-inflammatory cytokines in the

maternal-fetal unit (Bowen et al., 2002; Szukiewich, 2012). In our study

statistically significant correlations appeared only in term placentas: in term

placentas of the whole study (including term distress) TNFα positively

correlated only with IL-1α while in the study group G1 (term) besides this one

appeared another positive correlation between TNFα and IL-10. Simultaneous

increase of IL-10 and TNFα in cases of infectious stimuli (Barrera et al., 2012;

48

Mitchell et al., 2012) can possibly be referable to our findings in G1 (term) as

increase of TNFα and IL-10 during microbial colonization at delivery. In our

study group G3 (distress) was found a statistically significant positive

correlation between the rank values of IL-10 and the rank values of tissue

degrading enzyme MMP9; in the literature there are in vitro data, describing

IL-10 to decrease MMP2 and MMP9 and suggesting its therapeutic application

to arrest pre-term delivery (Fortunato et al., 2001). Taking into account that

immediate termination of pregnancies were the best tactics for particular

clinical situations, results should not be percieved as controversial, especially

as in those placentas the rank values of IL-10 positively correlated also with the

rank values of Collagen IV, suggesting discrepancy of maintenence and

interruption processes of pregnancy. Looking for correlations between the rank

values of Collagen IV and other molecular factors in the study group G1 (term)

we found positive correlations with the rank values of HGF and IGFR1, while

in the study groups G2 (pre-term) and G3 (distress) appeared positive

correlations with the rank values of FGFR1, suggesting presence of Collagen

IV in term placentas indicating placental growth and regeneration while in pre-

term and distress placentas – with fibrosis and possible decrease of

functionality. Data agree with described placental fibrosis in cases of increased

expression of Collagen IV (Chen and Aplin, 2003; Chen et al., 2005).

Statistically significant positive correlation between the rank values of MMP9

and IL-10 suggests the role of MMP9 in the initiation of labor in cases with

sustaining maternal tolerance against fetal allograft, coinciding studies on the

role of MMP9 in initiation of pre-term labor (Romero et al., 2002; Weiss et al.,

2007; Karthikeyan et al., 2012). We recognized MMP9 as a significant

molecular factor of the maternal-fetal unit, playing a significant role in the

initiation of labor to achieve better perinatal result. Rank values of the products

of a homeobox gene HoxB3 in the study group G1 (term) significantly

49

correlated with the rank values of IGFR1 and Collagen IV; in the study group

G3 (distress) they showed positive correlations with the rank values of MMP9,

allowing us to assume role of HoxB3 in sustaining of tissue integrity. Such

correlations have not been described in the literature; on the other hand we did

not find described negative correlations between Hox gene products and pro-

inflammatory cytokines (Sarno et al., 2006; Sarno et al., 2009). To our mind

role of Hox genes and presence of their products in the maternal-fetal unit is a

worthwhile subject for further research, possibly disclosing view of fetal

growth.

7.4. Clinical data

In the study group G1 (term) and in the whole study were found

negative correlations between the rank values of FGFR1 and neonatal blood

glucose. The study group G1 (term) included normal term deliveries with

healthy children and no hypoglycaemia, therefore finding rather agree with

studies, that have found more pronounced expression of FGFR in placentas of

macrosomic neonates of diabetic mothers (Grissa et al., 2010) and suggest

possible cases of undetected glucose intolerance. In the study group G2 (pre-

term) we found positive correlations between the rank values of pro-

inflammatory cytokine IL-1α and ROM; the rank values of TNFα did not show

such correlations, supporting our suggestion regarding specific reactions of pro-

inflammatory cytokines in placental tissues. Rank values of a pro-inflammatory

cytokine IL-1α in pre-term placentas showed significant negative correlation

with neonatal pH and 1st minute Apgar score coinciding with studies, having

found increase of IL-1 and IL-6 in maternal and fetal blood in acidotic

circumstances (Prout et al., 2010). In the study group G3 (distress) appeared

statistically significant negative correlation of the rank values of TNFα and

50

neonatal blood pH, possibly coinciding studies, having found higher level of

TNFα in children with problems of neurologic and motoric development

(Carpentier et al., 2011). Our results suggest gestation related pathways of

inflammation. In the study group G3 (distress) we found a significant positive

correlation of the rank values of placental TNFα and neonatal blood glucose,

matching results of other studies, having found increased production of TNFα

in placentas of patients with a higher blood glucose (Moreli et al., 2012) and

possibly indicate undiagnosed glucose intolerance in pregnancy. The highest

mean rank value of a tissue degrading enzyme MMP9 appeared in the study

group G3 (distress); difference with the mean rank value of the study group G1

(term) was statistically significant. Assessed and not found deviations of

MMP9 expression in vitro in cases of oxygen deprivation (Merchant et al.,

2004) is not controversial to our results and should be undertaken as an

affirmation of compensatory potential of the placenta. Evaluating correlations

of the rank values of HoxB3 products with clinical parameters in the study

group G3 (distress) we found a positive correlation with maternal body height,

that could be undertaken as a coincidence if having been find alone; previously

described correlations with neonatal anthropometrical parameters suggest

substantial impact. These data are unique and indicate factors of fetal

longitudinal growth and not just impact of Hox genes on placental growth and

development (Zhang et al., 2002; Amesse et al., 2003).

51

8. CONCLUSIONS

1. Patients of pre-term deliveries previously had more pregnancies (not

deliveries), suggesting termination of pregnancy as a risk factor for

consecutive pre-term delivery; maternal anthropological and social

parameters did not differ. Weight of a pre-term placenta directly

proportional to the gestational age and positively correlate with neonatal

anthrolometrical data; term placentas do not show such correlations. The

main neonatal anthropometrical parameters are directly proportional to the

gestational age.

2. Growth factor and receptor IGF1, IGFR1, HGF and FGFR1 positive cells

are seen in almost all the placentas from 22 weeks of pregnancy till term,

indicating their permanent growth potential. Decrease of IGF1, IGFR1

and HGF positive cells with advanced gestation seems to be related to

placental maturation/ ageing. Amount of FGFR1 positive cells increase

with advanced gestation, indicating placental maturity, especially close to

term.

3. Dissimilar correlations of the rank values of IL-1α and TNFα show

placental capability to produce pro-inflammatory cytokines selectively,

depending on the process and gestation. Decrease of IL-10 positive cells

in placentas of ongoing pregnancy emphasize significance of maternal

immune tolerance in late second trimester and early third trimester.

4. Apoptosis is seen in placentas of various gestational ages and decreases

with ongoing pregnancy, suggesting switch to another way of cellular

disposal. Smaller amount of apoptotic cells was seen in patients with

histories of more pregnancies, indicating restricted capabilities of those

placentas to provide non-destructive disposal of cells.

52

5. Component of the basement membrane Collagen IV is seen in placentas

of various gestational ages with a significant increase towards term,

suggesting development of placental barrier.

6. Tissue degrading enzyme MMP9 positive cells are seen in placentas of

various gestational ages; their significant increase in distress

circumstances indicate possible role of placental MMP9 in natural

termination of pregnancy in different terms.

7. Homeobox gene HoxB3 product positive cells are seen in all the placentas

from 22 weeks till term; their amount does not correlate with the

gestational age, but with some neonatal anthropometrical parameters,

suggesting their role in the fetal longitudinal growth.

8. Normal term placentas regularly present smaller amount of IGF1, IGFR1

and HGF positive cells and larger amount of FGFR1 positive cells. More

advanced maternal age, number of pregnancies and deliveries are

associated with an increased amount of TNFα positive cells, suggesting

idea of the risk for ascending infections in those patients. Smaller number

of apoptotic cells in placentas of patients with larger number of

pregnancies suggest decrease of programming capabilities in consecutive

pregnancies. Increase of MMP9 positive cells in placentas of neonates

with larger body masses and chest circumferences indicate increeased

tissue strength of those placentas.

9. Pre-term placentas are characterized by a higher amount of IGF1 and

IGFR1 positive cells, indicating significant growth capabilities of those

placentas. Correlations with the rank values of IL-1α describe their

susceptibility to infectious agents; larger number of apoptotic cells

suggest active remodeling processes in this gestational time.

10. Placentas after significant fetal distress are characterized by imbalance of

pro- and anti-inflammatory cytokines, possibly having led to impairment

53

of placental and fetal growth and decrease of placental defence. Positive

correlations of the rank values of HGF positive cells with FGFR1 suggest

placental fibrosis in regeneration processes. Large amount of tissue

degrading enzyme MMP9 positive cells in distress placentas suggest its

role in natural triggering of labor in circumstances of fetal threat, possibly

influencing fetal well-being.

54

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10. PUBLICATIONS AND PRESENTATIONS Articles

1. Kreicberga I., Pilmane M., Rezeberga D. Histochemistry of placenta:

deeper understanding of molecular processes, having possible impact on

the physical development of fetus. Papers on Anthropology. 2010; XIX:

230-242.

2. Mohangoo A. D., Buitendijk S. E., Szamotulska K., Chalmers J., Irgens L.

M., Bolumar F., Nijhuis J. G., Zeitlin J., Kreicberga I. Gestational age

patterns of fetal and neonatal mortality in Europe: results from the Euro-

Peristat project. PLoS One. 2011; 6 (11): e24727. doi: 10.1371/ journal.

pone. 0024727.

3. Kreicberga I., Pilmane M., Rezeberga D. Important for clinic molecular

events in placenta. Proceedings of the XXII European congress of Perinatal

Medicine. 2010; 259-263.

4. Kreicberga I., Pilmane M., Rezeberga D. Cytokines, apoptosis and growth

factors in post-delivery placentas. Proceedings of XXII European Congress

of Obstetrics and Gynecology. 2012; 83-88.

5. Kreicberga I., Pilmane M., Rezeberga D., Expression of Insulin-like

growth factor 1 (IGF1) and its receptor (IGFR1) in two extremely pre-term

placentas. Acta Chirurgica Latviensis, 2013; (13): 74-76.

Abstracts for Latvian congresses and conferences

1. Kreicberga I., Franckeviča I., Pilmane M., Rezeberga D. Makroskopiskas.

mikroskopiskas un imūnhistoķīmiskas izmaiņas placentā normāli

noritošas grūtniecības gadījumā. To saistība ar augļa veselības stāvokli.

73

Abstract book of the scientific conference of Riga Stradins University,

2009: 44.

2. Kreicberga I., Pilmane M., Rezeberga D. Pēcdzemdību placentas

ekstracellulārās vides indikatoru saistība ar klīnisko atradni. Abstract book

of the scientific conference of Riga Stradins University, 2010; 217.

3. Kreicberga I., Pilmane M., Rezeberga D. Insulīnam līdzīgā augšanas

faktora un tā receptora pozitīvas struktūras dažāda gestācijas laika

placentās un to saistība ar jaundzimušo antropometriskajiem parametriem.

Abstract book of the scientific conference of Riga Stradins University,

2011.

4. Kreicberga I., Pilmane M., Rezeberga D. Imūnās atbildes citokīni dažāda

gestācijas vecuma pēcdzemdību placentās. Abstract book of the scientific

conference of Riga Stradins University, 2012; 206.

5. Kreicberga I., Pilmane M., Rezeberga D. Matricas metālproteināze MMP9

dažāda gestācijas vecuma pēcdzemdību placentās. Abstract book of the

scientific conference of Riga Stradins University, 2013.

Abstracts for international congresses and conferences

1. Kreicberga I., Pilmane M., Rezeberga D. Cyto-chemical factors in placenta

at the time of delivery, indicating infection associated risk for pre-term

delivery. Abstract book of the Baltic Morphology 4th scientific conference,

2007.

2. Eihenberga S., Kreicberga I., Rezeberga D., Melderis I., Franckeviča I.

Placentas makroskopiskā un mikroskopiskā izmeklēšana klīniskajā praksē.

Abstract book of the 5th Congress of Latvian Obstetricians and

Gynecologists. Baltic International conference in Obstetrics and

Gynecology, 2008; 29.

74

3. Kreicberga I., Pilmane M., Rezeberga D. Apoptozi veicinošo citoķīmisko

marķieru noteikšana placentā. Abstract book of thew 5th Congress of

Latvian Obstetricians and Gynecologists. Baltic International conference in

Obstetrics and Gynecology, 2008; 30.

4. Kreicberga I., Pilmane M., Rezeberga D. Cyto-chemical markers in

placenta at the time of delivery. indicating risk factors for increased

apoptotic cell death. Abstract book of the 9th World Congress of Perinatal

Medicine, 2009.

5. Kreicberga I., Pilmane M., Rezeberga M. Correlation of cytokines in

placentas with the clinical findings. J Matern-Fetal Neo M, 2010; 23

(Suppl 1): 362-363.

6. Kreicberga I., Pilmane M., Rezeberga D. Immunohistochemical (IHC)

detection of HoxB3 genes in post-delivery placentas of various gestational

ages. Earl Hum Dev, 2010; 86 (Suppl.): 52-53.

7. Kreicberga I., Pilmane M., Rezeberga D. Neonate and Insulin-like growth

factor 1 (IGF1) and receptor (IGFR1). Abstract book of the 1st Baltic

Pediatric congress together with Spring conference of European Academy

of Pediatrics (EAP). Annual conference of European Confederation of

Primary Care Paediatricians (ECPCP), 2011; 65-66.

8. Kreicberga I., Pilmane M., Rezeberga D. Hepatocyte growth factor (HGF)

in the placentas of different gestational ages. Abstract book of the 6th

scientific meeting of Baltic Morphology, 2011; 32.

9. Kreicberga I., Pilmane M., Rezeberga D. Cilvēka morfoģenēzi noteicoša

gēna HoxB3 imūnhistoķīmiska identifikācija dažādu gestācijas laiku

pēcdzemdību placentās. Abstract book of the 6th Congress of Latvian

Gynecologists and Obstetricians. 4th Joint Royal College of Obstetricians

and Gynecologists (RCOG)/ Latvian Association of Gynecologists and

Obstetricians Eurovision Conference, 2011; 48-49.

75

10. Kreicberga I., Pilmane M., Rezeberga D. Cytokines, apoptosis and growth

factors in post-delivery placentas. Abstract book of the 22nd European

Congress of Obstetrics and Gynaecology, 2012.

Poster and oral presentations in Latvian congresses and conferences

1. Makroskopiskas, mikroskopiskas un imūnhistoķīmiskas izmaiņas placentā

normāli noritošas grūtniecības gadījumā. To saistība ar augļa veselības

stāvokli. Poster presentation in the scientific conference of Riga Stradins

University, Riga, Latvia, 2009.

2. Pēcdzemdību placentas ekstracellulārās vides indikatoru saistība ar

klīnisko atradni. Oral presentation in the scientific conference of Riga

Stradins University, 2010.

3. Insulīnam līdzīgā augšanas faktora un tā receptora pozitīvas struktūras

dažāda gestācijas laika placentās un to saistība ar jaundzimušo

antropometriskajiem parametriem. Oral presentation in the scientific

conference of Riga Stradins University, Riga, Latvia, 2011.

4. Imūnās atbildes citokīni dažāda gestācijas vecuma pēcdzemdību placentās.

Oral presentation in the scientific conference of Riga Stradins University,

Riga, Latvia, 2012.

5. Matricas metālproteināze MMP9 dažāda gestācijas vecuma pēcdzemdību

placentās. Oral presentation in the scientific conference of Riga Stradins

University, 2013.

Poster and oral presentations in international congresses and conferences

1. Apoptozi veicinošo citoķīmisko marķieru noteikšana placentā. I.

Kreicberga, M. Pilmane, D.Rezeberga. Poster presentation in the 5th

76

Congress of Latvian Obstetricians and Gynecologists. Baltic International

conference in Obstetrics and Gynecology. Riga. Latvia. October 10-11,

2008.

2. Cyto-chemical markers in placenta at the time of delivery, indicating risk

factors for increased apoptotic cell death. Poster presentation. 9th World

Congress of Perinatal Medicine. Berlin. Germany. October 24-28, 2009.

3. Correlation of cytokines in placentas with the clinical findings. Poster

presentation in the XXII European Congress of Perinatal Medicine.

Granada, Spain, May 26-29, 2010.

4. Immunohistochemical (IHC) detection of HoxB3 genes in post-delivery

placentas of various gestational ages. Oral presentation in the 2nd

International congress of UENPS. Istanbul, Turkey, November 15-17,

2010.

5. Neonate and Insulin-like growth factor 1 (IGF1) and receptor (IGFR1).

Oral presentation in the 1st Baltic Pediatric congress together with Spring

conference of European Academy of Pediatrics (EAP). Annual conference

of European Confederation of Primary Care Paediatricians (ECPCP).

Vilnius, Lithuania, May 19-22, 2011.

6. Hepatocyte growth factor (HGF) in the placentas of different gestational

ages. Oral presentation in the 6th scientific meeting of Baltic Morphology.

Tartu, Estonia, September 22-23, 2011.

7. Cilvēka morfoģenēzi noteicoša gēna HoxB3 imūnhistoķīmiska

identifikācija dažādu gestācijas laiku pēcdzemdību placentās. Oral

presentation in the 6th Congress of Latvian Gynecologists and

Obstetricians. 4th Joint Royal College of Obstetricians and Gynecologists

(RCOG)/ Latvian Association of Gynecologists and Obstetricians

Eurovision Conference. Riga, Latvia, October 13-15, 2011.

77

8. Cytokines, apoptosis and growth factors in post-delivery placentas. Oral

presentation in the 22nd European Congress of Obstetrics and

Gynaecology. Tallinn, Estonia, May 9-12, 2012.

78

11. ACKNOWLEDGEMENTS

This research stands on many shoulders, and I would like to

acknowledge the vigorous contribution of people, who have shared their

competence and provided support to advance this research.

I would like to express sincere gratitude to Professor Māra Pilmane and

Professor Dace Rezeberga for their inspiring supervision of my research and

Thesis writing. Without their permanent insistence and unconditional assistance

this research would not have been fully developed.

I am very grateful to Associate professor Ilze Štrumfa, Professors Daiva

Vaitkiene and Andres Arend for readiness to devote their valuable time for

evaluation of the Thesis.

I would like to thank the chief of the Board of the Maternity hospital Dr.

Ilze Lietuviete for her interest in the success of this study; my colleagues

obstetricians gynecologists of the Riga Maternity hospital, especially Marija

Holodova and Juris Beļevičs, who took placental preparations in the middle

of the night, according to requirements stated in the study design. I truly

appreciate the committment of all the medical staff of the Maternity hospital,

who have devoted their time in promoting the realization of this research study.

Many thanks to the staff of Institute of Anatomy and Anthropology,

especially Natālija Moroza and Elita Jakovicka, who have invested their best

efforts in the assistance of my research study.

I am sincerely grateful to all the members of my family, patiently

enduring continuous mental absence of a physically present wife, mother and

daughter; I am thankful to my sons George and David for having helped me

with certain issues of Thesis writing.

I am obliged to the project of the European Social Fund “Atbalsts

doktorantiem studiju programmas apguvei un zinātniskā grāda ieguvei Rīgas

79

Stradiņa universitātē” (agreement Nr. 2009/0147/1DP/1.1.2.1.2/09/IPIA/

VIAA/009), providing substantial financial support and ensuring successful

completion of this work.

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